Methods for the purification and detection of single nucleotide KRAS mutations on extrachromosomal circular DNA in human plasma.

BACKGROUNDS: Despite recent advances, many cancers are still detected too late for curative treatment. There is, therefore, a need for the development of new diagnostic methods and biomarkers. One approach may arise from the detection of extrachromosomal circular DNA (eccDNA), which is part of cell-free DNA in human plasma. AIMS: First, we assessed and compared two methods for the purification of eccDNA from plasma. Second, we tested for an easy diagnostic application of eccDNA liquid biopsy-based assays. MATERIALS & METHODS: For the comparison we tested a solid-phase silica purification method and a phenol/chloroform method with salt precipitation. For the diagnostic application of eccDNA we developed and tested a qPCR primer-based SNP detection system, for the detection of two well-established cancer-causing KRAS mutations (G12V and G12R) on circular DNA. This investigation was supported by purifying, sequencing, and analysing clinical plasma samples for eccDNAs containing KRAS mutant alleles in 0.5 mL plasma from 16 pancreatic ductal adenocarcinoma patients and 19 healthy controls. RESULTS: In our method comparison we observed, that following exonuclease treatment a lower eccDNA yield was found for the phenol/chloroform method (15.7%-26.7%) compared with the solid-phase purification approach (47.8%-65.9%). For the diagnostic application of eccDNA tests, the sensitivity of the tested qPCR assay only reached ~10-3 in a background of 105 wild type (wt) KRAS circular entities, which was not improved by general amplification or primer-based inhibition of wt KRAS amplification. Furthermore, we did not detect eccDNA containing KRAS in any of the clinical samples. DISCUSSION: A potential explanation for our inability to detect any KRAS mutations in the clinical samples may be related to the general low abundance of eccDNA in plasma. CONCLUSION: Taken together our results provide a benchmark for eccDNA purification methods while raising the question of what is required for the optimal fast and sensitive detection of SNP mutations on eccDNA with greater sensitivity than primer-based qPCR detection.

Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies.

BACKGROUND: The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. RESULTS: We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. CONCLUSIONS: A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

Personalized genome assembly for accurate cancer somatic mutation discovery using tumor-normal paired reference samples.

BACKGROUND: The use of a personalized haplotype-specific genome assembly, rather than an unrelated, mosaic genome like GRCh38, as a reference for detecting the full spectrum of somatic events from cancers has long been advocated but has never been explored in tumor-normal paired samples. Here, we provide the first demonstrated use of de novo assembled personalized genome as a reference for cancer mutation detection and quantifying the effects of the reference genomes on the accuracy of somatic mutation detection. RESULTS: We generate de novo assemblies of the first tumor-normal paired genomes, both nuclear and mitochondrial, derived from the same individual with triple negative breast cancer. The personalized genome was chromosomal scale, haplotype phased, and annotated. We demonstrate that it provides individual specific haplotypes for complex regions and medically relevant genes. We illustrate that the personalized genome reference not only improves read alignments for both short-read and long-read sequencing data but also ameliorates the detection accuracy of somatic SNVs and SVs. We identify the equivalent somatic mutation calls between two genome references and uncover novel somatic mutations only when personalized genome assembly is used as a reference. CONCLUSIONS: Our findings demonstrate that use of a personalized genome with individual-specific haplotypes is essential for accurate detection of the full spectrum of somatic mutations in the paired tumor-normal samples. The unique resource and methodology established in this study will be beneficial to the development of precision oncology medicine not only for breast cancer, but also for other cancers.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

BACKGROUND: Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. RESULTS: In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. CONCLUSIONS: The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.

A standard strategy to discover somatic mutations in a cancer genome is to use next-generation sequencing (NGS) technologies to sequence the tumor tissue and its matched normal (commonly blood or adjacent normal tissue) for side-by-side comparison. However, when interrogating entire genomes (or even just the coding regions), the number of sequencing errors easily outnumbers the number of real somatic mutations by orders of magnitudes. Here, we describe SomaticSeq, which incorporates multiple somatic mutation detection algorithms and then uses machine learning to vastly improve the accuracy of the somatic mutation call sets.

Concordance of Genomic Alterations by Next-Generation Sequencing in Tumor Tissue versus Cell-Free DNA in Stage I-IV Non-Small Cell Lung Cancer.

Molecular biomarkers hold promise for personalization of cancer treatment. However, a typical tumor biopsy may be difficult to acquire and may not capture genetic variations within or across tumors. Given these limitations, tumor genotyping using next-generation sequencing of plasma-derived circulating tumor (ct)-DNA has the potential to transform non-small cell lung cancer (NSCLC) management. Importantly, mutations detected in biopsied tissue must also be detected in plasma-derived ctDNA at different disease stages. Using the AVENIO ctDNA Surveillance kit (research use only), mutations in ctDNA from NSCLC subjects were compared with those identified in matched tumor tissue samples, retrospectively. Plasma and tissue samples were collected from 141 treatment-naïve NSCLC subjects (stage I, n = 48; stage II, n = 37; stage III, n = 33; stage IV, n = 23). In plasma samples, the median numbers of variants per subject were 4, 6, 8, and 9 in those with stage I, II, III, and IV disease, respectively. The corresponding values in tissue samples were 5, 5, 6, and 4. The overall tissue-plasma concordance of stage II through IV was 62.2% by AVENIO software call. On multivariate analysis, concordance was positively and significantly associated with tumor size and cancer stage. Next-generation sequencing-based analyses with the AVENIO ctDNA Surveillance kit could be an alternative approach to detecting genetic variations in plasma-derived ctDNA isolated from NSCLC subjects.

Systematic comparison of two whole-genome amplification methods for targeted next-generation sequencing using frozen and FFPE normal and cancer tissues.

Sequencing key cancer-driver genes using formalin-fixed, paraffin-embedded (FFPE) cancer tissues is becoming the standard for identifying the best treatment regimen. However, about 25% of all samples are rejected for genetic analyses for reasons that include too little tissue to extract enough high quality DNA. One way to overcome this is to do whole-genome amplification (WGA) in clinical samples, but only limited studies have tested different WGA methods in FFPE cancer specimens using targeted next-generation sequencing (NGS). We therefore tested the two most commonly used WGA methods, multiple displacement amplification (MDA-Qiagen REPLI-g kit) and the hybrid or modified PCR-based method (Sigma/Rubicon Genomics Inc. GenomePlex kit) in FFPE normal and tumor tissue specimens. For the normalized copy number analysis, the FFPE process caused none or very minimal bias. Variations in copy number were minimal in samples amplified using the GenomePlex kit, but they were statistically significantly higher in samples amplified using the REPLI-g kit. The pattern was similar for variant allele frequencies across the samples, which was minimal for the GenomePlex kit but highly variable for the REPLI-g kit. These findings suggest that each WGA method should be tested thoroughly before using it for clinical cancer samples.

Whole exome and targeted deep sequencing identify genome-wide allelic loss and frequent SETDB1 mutations in malignant pleural mesotheliomas.

Malignant pleural mesothelioma (MPM), a rare malignancy with a poor prognosis, is mainly caused by exposure to asbestos or other organic fibers, but the underlying genetic mechanism is not fully understood. Genetic alterations and causes for multiple primary cancer development including MPM are unknown. We used whole exome sequencing to identify somatic mutations in a patient with MPM and two additional primary cancers who had no evidence of venous, arterial, lymphovascular, or perineural invasion indicating dissemination of a primary lung cancer to the pleura. We found that the MPM had R282W, a key TP53 mutation, and genome-wide allelic loss or loss of heterozygosity, a distinct genomic alteration not previously described in MPM. We identified frequent inactivating SETDB1 mutations in this patient and in 68 additional MPM patients (mutation frequency: 10%, 7/69) by targeted deep sequencing. Our observations suggest the possibility of a new genetic mechanism in the development of either MPM or multiple primary cancers. The frequent SETDB1 inactivating mutations suggest there could be new diagnostic or therapeutic options for MPM.

An ensemble approach to accurately detect somatic mutations using SomaticSeq.

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated.

CK2α, over-expressed in human malignant pleural mesothelioma, regulates the Hedgehog signaling pathway in mesothelioma cells.

BACKGROUND: The Hedgehog (Hh) signaling pathway has been implicated in stem cell maintenance and its activation is aberrant in several types of cancer including mesothelioma. Protein kinase CK2 affects several cell signaling pathways through the mechanism of phosphorylation. METHODS: Protein and mRNA levels of CK2α and Gli1 were tested by quantitative RT-PCR and immunohistochemistry staining in mesothelioma samples and cell lines. Down-regulated Gli1 expression and transcriptional activity were demonstrated by RT-PCR, Western blot and luciferase reporter assay. RESULTS: In this study, we show that CK2α is over-expressed and a positive regulator of Hegdehog/Gli1 signaling in human malignant pleural mesothelioma. First of all, we found that the mRNA levels of CK2α and Gli1 were broadly elevated and correlated (n = 52, r = 0.401, P < 0.05), compared with LP9 (a normal mesothelial cell line). We then investigated their expression at the protein level, and found that all the 7 mesothelioma cell lines tested showed positive staining in CK2α and Gli1 immunohistochemistry. Correlation analysis by Pearson test for CK2α and Gli1 expression in the 75 mesothelioma tumors and the 7 mesothelioma cell lines showed that the two protein expression was significantly correlated (n = 82, r = 0.554, P < 0.01). Furthermore, we demonstrated that Gli1 expression and transcriptional activity were down-regulated after CK2α was silenced in two mesothelioma cell lines (H28 and H2052). CK2α siRNA also down-regulated the expression of Hh target genes in these cell lines. Moreover, treatment with a small-molecule CK2α inhibitor CX-4945 led to dose-dependent inhibition of Gli1 expression and transcriptional activity. Conversely, forced over-expression of CK2α resulted in an increase in Gli1 transcriptional activity in H28 cells. CONCLUSIONS: Thus, we report for the first time that over-expressed CK2α positively regulate Hh/Gli1 signaling in human mesothelioma.

Lung tumourigenesis in a conditional Cul4A transgenic mouse model.

Cullin4A (Cul4A) is a scaffold protein that assembles cullin-RING ubiquitin ligase (E3) complexes and regulates many cellular events, including cell survival, development, growth and cell cycle control. Our previous study suggested that Cul4A is oncogenic in vitro, but its oncogenic role in vivo has not been studied. Here, we used a Cul4A transgenic mouse model to study the potential oncogenic role of Cul4A in lung tumour development. After Cul4A over-expression was induced in the lungs for 32 weeks, atypical epithelial cells were observed. After 40 weeks, lung tumours were visible and were characterized as grade I or II adenocarcinomas. Immunohistochemistry (IHC) revealed decreased levels of Cul4A-associated proteins p21(CIP1) and tumour suppressor p19(ARF) in the lung tumours, suggesting that Cul4A regulated their expression in these tumours. Increased levels of p27(KIP1) and p16(INK4a) were also detected in these tumours. Moreover, the protein level of DNA replication licensing factor CDT1 was decreased. Genomic instability in the lung tumours was further analysed by the results from pericentrin protein expression and array comparative genomic hybridization analysis. Furthermore, knocking down Cul4A expression in lung cancer H2170 cells increased their sensitivity to the chemotherapy drug cisplatin in vitro, suggesting that Cul4A over-expression is associated with cisplatin resistance in the cancer cells. Our findings indicate that Cul4A is oncogenic in vivo, and this Cul4A mouse model is a tool in understanding the mechanisms of Cul4A in human cancers and for testing experimental therapies targeting Cul4A.

Comprehensive genomic analyses of a metastatic colon cancer to the lung by whole exome sequencing and gene expression analysis.

We performed whole exome sequencing and gene expression analysis on a metastatic colon cancer to the lung, along with the adjacent normal tissue of the lung. Whole exome sequencing uncovered 71 high-confidence non­synonymous mutations. We selected 16 mutation candidates, and 13 out of 16 mutations were validated by targeted deep sequencing using the Ion Torrent PGM customized AmpliSeq panel. By integrating mutation, copy number and gene expression microarray data, we identified a JAZF1 mutation with a gain-of-copy, suggesting its oncogenic potential for the lung metastasis from colon cancer. Our pathway analyses showed that the identified mutations closely reflected characteristics of the metastatic site (lung) while mRNA gene expression patterns kept genetic information of its primary tumor (colon). The most significant gene expression network was the 'Colorectal Cancer Metastasis Signaling', containing 6 (ADCY2, ADCY9, APC, GNB5, K-ras and LRP6) out of the 71 mutated genes. Some of these mutated genes (ADCY9, ADCY2, GNB5, K-ras, HDAC6 and ARHGEF17) also belong to the 'Phospholipase C Signaling' network, which suggests that this pathway and its mutated genes may contribute to a lung metastasis from colon cancer.

Gremlin is overexpressed in lung adenocarcinoma and increases cell growth and proliferation in normal lung cells.

BACKGROUND: Gremlin, a member of the Dan family of BMP antagonists, is a glycosylated extracellular protein. Previously Gremlin has been shown to play a role in dorsal-ventral patterning, in tissue remodeling, and recently in angiogenesis. Evidence has previously been presented showing both over- and under-expression of Gremlin in different tumor tissues. Here, we sought to quantify expression of Gremlin in cancers of the lung and performed in vitro experiments to check whether Gremlin promotes cell growth and proliferation. METHODOLOGY/PRINCIPAL FINDINGS: Expression of Gremlin in 161 matched tumor and normal lung cancer specimens is quantified by quantitative real-time PCR and protein level is measured by immunohistochemistry. GREM1 was transfected into lung fibroblast and epithelial cell lines to assess the impact of overexpression of Gremlin in vitro. RESULTS: Lung adenocarcinoma but not squamous cell carcinoma shows a significant increase in Gremlin expression by mRNA and protein level. Lung fibroblast and epithelial cell lines transfected with GREM1 show significantly increased cell proliferation. CONCLUSIONS/SIGNIFICANCE: Our data suggest that Gremlin acts in an oncogenic manner in lung adenocarcinoma and could hold promise as a new diagnostic marker or potential therapeutic target in lung AD or general thoracic malignancies.

A somatic TSHR mutation in a patient with lung adenocarcinoma with bronchioloalveolar carcinoma, coronary artery disease and severe chronic obstructive pulmonary disease.

In a screen for thoracic malignancy-associated markers, thyroid stimulating hormone receptor (TSHR) was identified as a candidate as it binds to the previously-characterized lung cancer marker NKX2-1. We screened for mutations in all coding regions of the TSHR gene in 96 lung adenocarcinoma samples and their matched adjacent normal lung samples. We found one patient with a somatic mutation at codon 458 (exon 10), which is located at the transmembrane domain where most TSHR mutations have been found in thyroid-related diseases. This patient had lung adenocarcinoma with BAC (bronchioloalveolar carcinoma) features in the setting of a prior medical history significant for carotid stenosis and severe chronic obstructive pulmonary disease (COPD). In order to characterize the genetic features of TSHR in lung cancer, we checked for TSHR expression and copy number in the 96 lung cancer tissues. TSHR protein expression was generally overexpressed in multiple thoracic malignancies (adenocarcinoma, squamous cell carcinoma and malignant pleural mesothelioma) by immunohistochemistry. Our data suggest that aberrant TSHR function may contribute to lung cancer development or a subgroup of lung cancer with specific clinical phenotypes.